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Evolving Electric Mobility: In-Depth Analysis of Integrated Electronic Control Unit Development in Electric Vehicles

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Abstract

The integrated Electronic Control Unit (ECU) plays a pivotal role in optimizing energy efficiency within electric vehicles (EVs) by coordinating various subsystems, including the Vehicle Control Unit (VCU), Electrical Power Steering (EPS), Electronic Stability Control (ESC), and Body Control Unit (BCU). Our comprehensive review deeply explores the various aspects of integrated ECUs and their sub-disciplines, emphasizing development approaches and algorithms specifically designed for mobility energy efficiency. The study shows the effect of various control units on energy efficiency by carefully examining case studies and real-world applications. Employing a critical assessment, the paper examines the advantages and disadvantages of various control systems utilized in electric vehicles (EVs), providing insight into their effectiveness in various situations. The presented study is typically related to the relationship between ECUs and their sub-branches through an integrated approach. By comparing current control approaches, the study offers a deep knowledge of the role of these units in improving vehicle performance, stability, and overall control. Furthermore, we evaluate the strengths and limitations of integrated ECU algorithms in the context of EV control by comparing them in a fair amount of detail. We identify important research gaps by integrating knowledge of multiple control areas in EV. The research effort will give researchers a path forward and make it possible to pinpoint the prospective dimensions that need further exploration in advancing the area of integrated ECUs in EVs.
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Digital Object Identifier 10.1109/ACCESS.2023.0322000
Evolving Electric Mobility: In-Depth Analysis of
Integrated Electronic Control Unit Development
in Electric Vehicles
SYED SHEHRYAR ALI NAQVI1, HARUN JAMIL1, NAEEM IQBAL2, SALABAT KHAN3, MURAD ALI
KHAN4, FAIZA QAYYUM4, and DO-HYEUN KIM1,4,*
1Department of Electronics Engineering, Jeju National University, Jeju, 63243, Republic of Korea, syedshehryar@stu.jejunu.ac.kr, 1harunjamil@gmail.com
2School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, BT7 1NN Belfast, U.K, n.iqbal@qub.ac.uk
3(Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan) (Big Data Research Center, Jeju National
University, Jeju 63243, Republic of Korea)
4Department of Computer Engineering, Jeju National University, Jeju, 63243, Republic of Korea, muradali@stu.jejunu.ac.kr, faizaqayyum@jejunu.ac.kr
Corresponding author: D.H. Kim (kimdh@jejunu.ac.kr).
This work was supported by Electronics and Telecommunications Research Institute(ETRI) grant funded by the Korean government.
[23ZD1160, Regional Industry ICT Convergence Technology Advancement and Support Project in Daegu-GyeongBuk (Mobility)
ABSTRACT
The integrated Electronic Control Unit (ECU) plays a pivotal role in optimizing energy efficiency within
electric vehicles (EVs) by coordinating various subsystems, including the Vehicle Control Unit (VCU),
Electrical Power Steering (EPS), Electronic Stability Control (ESC), and Body Control Unit (BCU). Our
comprehensive review deeply explores the various aspects of integrated ECUs and their sub-disciplines,
emphasizing development approaches and algorithms specifically designed for mobility energy efficiency.
The study shows the effect of various control units on energy efficiency by carefully examining case
studies and real-world applications. Employing a critical assessment, the paper examines the advantages
and disadvantages of various control systems utilized in electric vehicles (EVs), providing insight into their
effectiveness in various situations. The presented study is typically related to the relationship between ECUs
and their sub-branches through an integrated approach. By comparing current control approaches, the study
offers a deep knowledge of the role of these units in improving vehicle performance, stability, and overall
control. Furthermore, we evaluate the strengths and limitations of integrated ECU algorithms in the context of
EV control by comparing them in a fair amount of detail. We identify important research gaps by integrating
knowledge of multiple control areas in EV. The research effort will give researchers a path forward and
make it possible to pinpoint the prospective dimensions that need further exploration in advancing the area
of integrated ECUs in EVs.
INDEX TERMS Electronic Control Unit, Vehicle Control Unit, Electrical Power Steering, Electronic
Stability Control, Body Control Unit.
I. INTRODUCTION
In recent years, the global automotive industry has witnessed
a significant shift towards electric vehicles (EVs) as a promis-
ing solution to mitigate environmental challenges associated
with traditional internal combustion engine (ICE) vehicles
[1], [2]. The need to reduce greenhouse gas emissions and
tackle climate change has propelled governments, organiza-
tions, and individuals to embrace cleaner and more sustain-
able transportation options. The transition to electric vehicles
offers a clear pathway to achieve these environmental goals,
primarily focusing on reducing carbon dioxide (CO2) emis-
sions, air pollutants, and dependence on fossil fuels [3].
Figure 1 shows the graph obtained using the data sourced
from the International Energy Agency’s Global EV Data
Explorer [4], which provides comprehensive information on
EV charging points, EV stock, EV sales, and other data of
different countries. For our analysis, we use stock and sales
data for battery electric vehicles (BEV) and plug-in hybrid
electric vehicles (PHEV) worldwide. In addition, it presents
the EV (BEV, PHEV) stocks and sales worldwide from 2020
in projections to 2030. We indicate the years 2020–2030
on the x-axis to provide the pattern of EV sales and stocks
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chronologically. The sales data for BEVs and PHEVs are
displayed on the left y-axis, highlighting the rising demand
for electric cars worldwide. The stocks of BEVs and PHEVs,
representing the total number of these cars in the market, are
on the right y-axis. The graph shows that with the sales of
2 million BEVs and 970,000 PHEVs, the EV market started
slowly in 2020. Sales of BEVs and PHEVs saw notable
increases by 2022, hitting 7.3 million and 2.9 million units,
respectively. While PHEV stocks steadily increased to 7.9
million units, BEV stocks reached 18 million. 2025 is pre-
dicted to be a significant turning point, with BEV sales rising
to 16 million units and exceeding PHEV sales of 4.5 million
units. In conclusion, the graph illustrates a steady growth
in EV adoption over the five years, with sales increasing
substantially yearly. The noteworthy finding is the increase
in BEV sales, which is more than PHEV sales. The graph
shows a sharp increase in BEV sales between 2025 and 2030,
pointing to a clear shift towards BEVs worldwide.
FIGURE 1. Overview of the EV statistics
The continuous advancements in electric vehicle technol-
ogy have further bolstered the case for their widespread adop-
tion and continued development. In [5], authors examined
how the market for EVs is changing, focusing on technol-
ogy developments and how they contribute to the goal of
a carbon-neutral society. With ongoing research and devel-
opment efforts, innovative solutions have emerged in areas
such as battery technology [6], charging infrastructure [7],
and control units, driving significant improvements in EV
performance, range, and overall user experience. In [6], Abro
et al. explores the most recent advances in battery technology
developments for EVs, including a thorough summary of the
latest advances in energy management, battery design, and
optimization. In [7], Acharige et al. provide insights into the
changing environment of EV charging systems by consider-
ing dedicated converter topologies, control techniques, and
standard compliance to improve charging efficiency and grid
support. Future trends indicate integrating advanced control
units in electric vehicles, enabling enhanced Vehicle-to-Grid
(V2G) capabilities, intelligent energy management systems,
and seamless connectivity with smart grids [8]. These devel-
opments aim to optimize energy consumption [9], minimize
charging times [10], and maximize the utilization of renew-
able energy sources, thereby strengthening the environmental
benefits of electric vehicles [11].
Electric vehicles (EVs) have witnessed significant ad-
vancements in control technologies, contributing to im-
proved performance, efficiency, and overall driving experi-
ence. Modern EVs incorporate various control technologies
to manage multiple aspects of their operation [12]. These
include powertrain control systems, energy management sys-
tems, regenerative braking control, and vehicle stability con-
trol. Powertrain control systems optimize the coordination
between the electric motor, battery, and other components
to deliver efficient and responsive power delivery [13]. En-
ergy management systems monitor and control the energy
flow within the vehicle, ensuring optimal utilization of the
battery’s capacity [14]. Regenerative braking control tech-
nologies capture kinetic energy during braking, converting it
into electrical energy for recharging the battery [15]. Vehicle
stability control systems utilize sensors and actuators to en-
hance safety and stability during acceleration, deceleration,
and cornering [16].
Much research on EV powertrain control systems has been
done recently in the literature. The authors of [13] provide
a novel method for hybrid Deep Reinforcement Learning
(DRL) strategy-based powertrain control optimization in Hy-
brid Electric Vehicles (HEVs). Using preexisting vehicle data,
the authors pre-train offline neural networks (NNs) combined
with an online DRL method. This combination method effec-
tively achieves optimal power source management for HEVs
while speeding up the learning process and improving fuel
efficiency in simulated instances. In [14], authors addressed
energy management systems in EVs. They carried out an
extensive investigation that centered on the creation of an En-
ergy Management System (EMS) for a plug-in hybrid electric
vehicle (HEV) through the use of deep learning techniques,
particularly Recurrent Neural Networks (RNNs). Based on
Real Driving Emissions (RDE) compliant vehicle missions,
the AI models were trained offline to decrease carbon diox-
ide emissions. The integration of regenerative braking con-
trol technology has also gained attention in recent research.
With an emphasis on energy savings and increased driving
distance, [15] investigated the regenerative braking control
approach for EVs with four in-wheel motors. The approach
involves splittingthe braking force between the hydraulic and
motor braking systems and between the front and back axles.
The efficacy of the suggested fuzzy control-based method,
which uses a Mamdani fuzzy controller, is confirmed by sim-
ulation, indicating that it can improve regenerative braking
efficiency for EVs. Within vehicle stability control systems,
the study of [16] is noteworthy. Their study concentrated
on the advantages of using axle electric motors for vehicle
stability control in hybrid electric vehicles (HEVs). It presents
a combined electrohydraulic braking and axle motor torque
control technique for differential braking and driving stability
management. The devised technique is compared with current
2VOLUME 11, 2023
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
vehicle stability control systems using real-time simulations
of intense steering maneuvers, assessing energy consumption,
performance, and stability characteristics for hybrid electric
sport utility vehicles. Our review study analyzes these dif-
ferent control technologies in a unified form under different
control units in EVs to evaluate their capabilities, benefits,
and challenges, advancing EV performance, efficiency, and
sustainability while ensuring optimal energy management
and vehicle control.
The significant contributions of our research study are
listed below:
The study provides a detailed review of different de-
velopment strategies and control algorithms that can be
adapted to efficiently control different EV control units.
The study highlights the configuration and sensors uti-
lized in each control unit of EVs.
Moreover, the comparison of different control algo-
rithms adopted to control EVs for mobility energy ef-
ficiency is presented.
Finally, the current research challenges are highlighted
to pave the way for future research directions.
The proposed study is organized as shown in Figure 2.
After the introduction section, development strategies and al-
gorithms of integrated ECU for mobility energy efficiency are
presented in section II followed by the development strategies
and algorithms of VCU, EPS, ESC, and BCU in the next
sections III, IV, V, and VI. The comparison of integrated ECU
algorithms for mobility energy efficiency is summarized in
section VII. Section VIII highlights the shortcomings of avail-
able solutions and future directions. Finally, the conclusion is
presented in section IX.
A. THE RESEARCH METHODOLOGY FOR ECU
This section highlights a comprehensive overview of the
detailed methodology followed for the research. Electronic
Control Unit (ECU) and its various types are chosen as
research methods for determining electric vehicle control
strategies.
B. PLANNING THE REVIEW
Review planning involves key components such as formulat-
ing research questions, identifying relevant data sources, and
creating search strings.
C. RESEARCH QUESTIONS
The research questions addressed in this review are:
RQ1: What are the different types of electronic control units
used in electric vehicles?
RQ2: What are the emerging control techniques in electronic
control units for electric vehicles?
D. DATA SOURCES FOR LITERATURE STUDY
An iterative technique was utilized to comprehensively ex-
plore various scientific databases and digital libraries. The
selection of these libraries was based on current research
trends. Data sources included are:
FIGURE 2. Detailed flow of the proposed research study
Google Scholar
Science Direct
Wiley Online Library
IEEE Xplore Library
E. SEARCH STRINGS
In this research study, we derived keywords from the existing
literature that aligned with our research questions to construct
the search strings. Boolean operators "OR" and "AND" com-
bine these keywords effectively. The search strings used to
explore online databases were as follows: (“Control Technol-
ogy” OR “Control Method”) AND (“Electronic Control Unit”
OR “Vehicle Control Unit” OR “Electrical Power Steering”
OR “Body Control Unit”) AND (“Electric Vehicles” OR
“Hybrid Electric Vehicles” OR “Autonomous Vehicle”) AND
(“Energy Management” OR “Energy Efficiency”).
II. INTEGRATION OF VARIOUS CONTROL UNITS IN EV
FOR ENHANCING ENERGY EFFICIENCY
As Electric Vehicles (EVs) continue to gain popularity, the au-
tomotive industry is constantly evolving to meet the demands
of this emerging market. One crucial aspect of EV develop-
ment is integrating various control units to ensure the efficient
and coordinated functioning of different vehicle systems. In
this context, the concept of an integrated Electronic Control
Unit (ECU) has emerged as a significant advancement in EV
technology. An integrated ECU combines multiple control
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units into one unit, streamlining communication and enhanc-
ing overall vehicle performance [17].
The integrated ECU in an electric vehicle typically incor-
porates four main control units: Vehicle Control Unit (VCU),
Electric Power Steering (EPS), Electronic Stability Control
(ESC), and Body Control Unit (BCU). All these control units
are crucial in ensuring the safety, efficiency, and smooth
operation of EVs [18].
Furthermore, the VCU acts as the central brain of the
integrated ECU system, managing and coordinating various
subsystems and components of the electric vehicle. It controls
the powertrain, battery management system, and other auxil-
iary systems. The VCU receives data from multiple sensors
and makes real-time decisions to optimize the vehicle’s per-
formance, including torque distribution, regenerativebraking,
and energy management [19].
The EPS control unit gives the driver precise and effortless
control over the vehicle’s steering. It uses sensors to measure
driver input and vehicle parameters, allowing the EPS system
to adjust the steering assistance accordingly. Integrating the
EPS control unit into the ECU allows the EV steering re-
sponse to coordinate seamlessly with other control functions,
enhancing overall vehicle stability and safety [20].
Similarly, the ESC control unit is designed to ensure vehi-
cle stability and prevent loss of control in critical situations. It
monitors various parameters, including steering angle, wheel
speed, and yaw rate. By applying individual wheel braking
and adjusting power distribution, the ESC control unit can
selectively intervene to correct understeer or oversteer con-
ditions. Integrating the ESC control unit with the integrated
ECU allows faster and more precise response times, enhanc-
ing vehicle safety during cornering and emergency maneuvers
[21].
Furthermore, the BCU controls various electrical and elec-
tronic systems within the vehicle’s body, including lighting,
climate control, door locks, windows, and other comfort and
convenience features. Integrating the Body Control Unit into
the integrated ECU allows the coordination and management
of these body-related functions to be streamlined, enhancing
overall vehicle functionality and user experience [22].
Moreover, Figure 3 shows the layout of ECU in EV. The
front axle is driven by a permanent magnet synchronous
motor connected to a transmission. The motor is regulated by
a Motor Control Unit (MCU) supervised by VCU. The Bat-
tery Management System (BMS) controls the battery pack,
supplying energy to move the vehicle and storing recovered
electric energy. Each wheel has a disc brake, monitored by
the Electronic Stability Control (ESC) system. Electric Power
Steering (EPS) assists in controlling the vehicle’s steering,
reducing energy consumption, and ensuring stability. Addi-
tionally, BCU manages various electronic systems in the ve-
hicle, including exterior and interior lighting and windshield
wipers. The ECUs coordinate the control of all these compo-
nents by communicating and exchanging their requests. [23].
FIGURE 3. Layout of Integrated ECU control in EV
A. ELECTRIC VEHICLE CONTROL CONCEPT
Electric vehicle control plays an important part in EVs’ effi-
cient operation and optimization. As the world shifts towards
sustainable transportation, developing advanced control sys-
tems for EVs becomes increasingly important. These control
systems are responsible for managing and coordinating dif-
ferent units of EV, such as the powertrain, energy storage sys-
tem, and auxiliary systems. By intelligently controlling these
components by introducing integrated ECU control, EVs can
achieve optimal performance, maximize energy efficiency,
and enhance the overall driving experience [24].
Effective control of electric vehicles offers several sig-
nificant advantages [25]. Firstly, it optimizes power distri-
bution and utilization within the vehicle, improving energy
efficiency. By effectively regulating the power transfer to the
motor using the battery, control systems can optimize en-
ergy usage, minimize losses, and extend the vehicle’s driving
range [26]. Moreover, efficient control of EVs helps achieve
better overall performance, including acceleration, braking,
and handling. Moreover, ensuring effective control of EVs
is critical for upholding vehicle safety. Control systems are
responsible for monitoring and protecting the battery pack,
ensuring proper charging and discharging behavior, and pre-
venting hazardous conditions, such as overcharging or over-
heating [27]. Additionally, control algorithms play a crucial
role in integrating various safety features, such as regenerative
braking and stability control systems, in enhancing the overall
safety of the EV [28].
To enable effective control of electric vehicles, certain
requirements must be fulfilled. These include precise sensing
and measurement systems to provide accurate data on vehicle
parameters like speed, position, battery state of charge, and
temperature [29]. Additionally, sophisticated control algo-
rithms are needed to process this data and make real-time
decisions regarding power distribution, torque control, energy
management, and vehicle dynamics. These algorithms should
be capable of adapting to changing driving conditions, opti-
4VOLUME 11, 2023
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mizing power flow, and ensuring the seamless integration of
different vehicle subsystems [30]. Moreover, electric vehicle
control requires a reliable and robust communication network
to facilitate the exchange of information between various
components, such as the battery management system, motor
controller, and vehicle control unit. This network enables co-
ordinated control actions and ensures the proper functioning
of the entire EV system [31].
B. ELECTRIC VEHICLE AND ITS TYPES
Much research is going on on electric vehicles, which promise
future solutions to environmental issues. The goals of these
research studies are the focus of many electric vehicle config-
urations and their control. Battery Electric Vehicles (BEVs)
run entirely on electricity and are powered by rechargeable
batteries. Hybrid Electric Vehicles (HEVs) combine an inter-
nal combustion engine with an electric motor and a battery;
Plug-in Hybrid Electric Vehicles (PHEVs) have both an inter-
nal combustion engine and a rechargeable battery, and Fuel
Cell Electric Vehicles (FCEVs) use a fuel cell to generate
electricity on board by combining hydrogen gas from a tank
with oxygen from the air are examples of EVs [23]. BEVs
often face challenges primarily related to their batteries, mak-
ing them more suitable for smaller electric vehicles with short
distances and lower speeds in local transportation. These ve-
hicles require smaller battery capacities. While cost remains
a significant concern, HEVs can meet consumer demands and
offer additional benefits. FCEVs have the potential to become
popular vehicles in the future. However, the technology is in
its early stages, with price and fueling infrastructure being
the main areas of concern [24]. The user’s daily commute,
availability of charging infrastructure, financial restraints,
and environmental concerns are some variables that influence
the choice of EV. While some consumers choose PHEVs
because of their flexibility and extended driving range, others
may emphasize lowering the environmental impact and go for
BEVs [8]. The EV types and their characteristics, along with
issues in each type and the example models, are explained in
Table 1.
C. EV CONFIGURATION AND SENSORS
Sensors play a vital role in electric vehicles (EVs) by provid-
ing crucial information to control units, enabling precise mon-
itoring and control of various subsystems [39]. These sensors
serve as the sensory organs of the EV, capturing real-time
data on key parameters such as speed, position, temperature,
battery state of charge, and environmental conditions. This in-
formation allows the control units to make informed decisions
and implement effective control strategies, enhancing electric
vehicles’ performance, efficiency, and safety [40].
The EV system can be categorized into three primary
blocks, shown in Figure 4. To perceive the surrounding en-
vironment, the EV is equipped with various sensors, which
are hardware devices responsible for collecting data. The
gathered sensor data is then processed in the optimal control
block, where different components work together to extract
meaningful information from the sensor inputs. The optimal
control block utilizes optimal control algorithms to generate
controlled commands for actuators. To ensure the following
path, the control module sends control commands to the
vehicle, thereby overseeing its movement and trajectory [41].
FIGURE 4. Block diagram of EV system based on installed sensing and
actuating devices
D. DEVELOPMENT STRATEGIES OF INTEGRATED ECU FOR
MOBILITY ENERGY EFFICIENCY
The development strategy for integrated ECU in an electric
vehicle involves ensuring the system is reliable, efficient, and
safe. To achieve these goals, the ECU must be designed to
operate in various conditions and environments, including
extreme temperatures, high humidity, and high vibrations.
The ECU must also be designed to handle large amounts
of data quickly and efficiently while maintaining low power
consumption. Additionally, the ECU must be designed with
safety in mind since it controls critical systems in the vehicle.
This involves implementing fail-safe mechanisms, such as
redundant sensors and controllers, to ensure that the vehicle
can operate safely in case of a failure inside the system.
Overall, the design of an ECU for an electric vehicle requires
a balance of performance, efficiency, and safety [42].
Figure 5 shows the main development strategies of inte-
grated ECU to improve mobility energy efficiency.
Development strategies for integrated ECU include predic-
tive analytics, optimal operation based on machine learning,
thermal management, power electronics, and energy recuper-
ation. Predictive analytics can be used to analyze data from
sensors and other sources to help optimize the operation of
the ECU and the electric vehicle. Predictive analytics can
anticipate driving behavior [43], road conditions for route
optimization [44], and other factors that can affect energy
consumption and adjust power distribution and battery man-
agement accordingly. Machine Learning (ML) algorithms
can be used to optimize the operation of the ECU and the
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TABLE 1. Critical analysis of the characteristics of EV types
Ref. Electric Vehicle Type Advantages Disadvantages Example Models
[24],
[32]
Battery Electric Vehicle
(BEV) No tailpipe emissions
Lower operating costs
Simple drivetrain with fewer mov-
ing parts
Limited driving range per charge
Long charging time
Insufficient charging infrastruc-
ture
High initial cost of batteries
Tesla Model S, Nissan
Leaf, Chevrolet Bolt
[24],
[33],
[34]
Hybrid Electric Vehicle
(HEV) Improved fuel efficiency
Regenerative braking reduces en-
ergy wastage
No need for external charging
Complex powertrain systems
Battery degradation over time
Reliance on fossil fuels for the in-
ternal combustion engine
Toyota Prius, Honda Ac-
cord Hybrid, Ford Fusion
Hybrid
[35],
[36]
Plug-in Hybrid Electric
Vehicle (PHEV) All-electric driving range for short
trips
Flexibility for longer trips with the
internal combustion engine
Regenerative braking and reduced
emissions
Additional weight and complexity
from larger batteries
Charging infrastructure availabil-
ity
Balancing power demands be-
tween electric and internal com-
bustion engines
Chevrolet Volt, Mitsubishi
Outlander PHEV, BMW i3
REx
[24],
[37],
[38]
Fuel Cell Electric Vehi-
cle (FCEV) Longer driving range compared to
BEVs
Fast refueling time
Zero tailpipe emissions
Limited hydrogen fueling infras-
tructure
High cost and limited availability
of fuel cell technology
Hydrogen production and storage
challenges
Toyota Mirai, Hyundai
Nexo, Honda Clarity Fuel
Cell
FIGURE 5. Taxonomy of the development strategies for integrated ECU
electric vehicle. ML can identify patterns in driving behav-
ior and road conditions and adjust the power distribution
and battery management accordingly. This can help to im-
prove energy efficiency and reduce carbon emissions [45].
Efficient thermal management of the ECU can help to re-
duce power consumption and improve energy efficiency. This
can be achieved using advanced cooling systems and heat-
dissipation materials [46]. Advanced power electronics, such
as wide-bandgap semiconductors, can improve the efficiency
of the ECU and the electric vehicle. Wide-bandgap semicon-
ductors have lower resistance and switching losses, which
results in less heat generation and higher efficiency [47].
Energy Recuperation involves using energy recuperation sys-
tems, such as regenerative braking, which can help capture
the energy that would otherwise be lost during braking and
recharge the battery. This can improve energy efficiency and
reduce the need for external charging [48].
Table 2 shows current developments in the field of EV
technology, with an emphasis on development techniques
for integrated ECU. The table highlights several noteworthy
research papers demonstrating innovative methods to im-
prove EV performance, energy efficiency, and sustainability.
The most common development strategies include machine
learning algorithms to analyze data from the ECU and pre-
dict driving behavior. This information can then be used to
optimize the performance of the EV and improve energy
efficiency [49]. Integrating real-world data combines data
from the ECU with real-world data, such as driving conditions
and weather information, to develop more accurate and robust
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control models [50]. Advanced electronics technologies, such
as field-programmable gate arrays (FPGAs), are used to de-
velop more efficient and powerful ECUs [51].
Table 2 offers a comprehensive overview of the latest
research on development strategies of Integrated ECU in
EVs. Bangroo et al.’s approach [52] utilizing AI-based pre-
dictive analytics stands out for its potential to enhance
decision-making, energy efficiency, and cybersecurity in
electric/hybrid vehicles. However, using large datasets raises
concerns about data privacy that need to be addressed for
practical implementation. Canal et al.’s study (Ref. [53])
integrating real ECU data with machine learning algo-
rithms demonstrates impressive accuracy in identifying driv-
ing patterns, emphasizing the real-world applicability of their
methodology. Nevertheless, concerns about overfitting and
the need for robust regularization techniques are vital aspects
to consider in further applications. Broatch et al.’s research
(Ref. [54]) on integrated thermal management systems stands
out for its incorporation of extensive real-world data, enhanc-
ing the accuracy of simulation results. However, expanding
the scope to other vehicle platforms like plug-in hybrids and
fully electric cars could offer more comprehensive insights.
Gong et al.’s emphasis (Ref. [55]) on modern power electron-
ics technologies in EV powertrains paves the way for future
innovations. Yet, the lack of quantitative analysis regarding
energy efficiency remains a notable limitation. Chengqun
et al.’s innovative regenerative braking system (Ref. [56])
stands out for its efficient energy recovery, especially for
aggressive driving styles, but could benefit from adaptability
enhancements considering diverse factors affecting regenera-
tive braking efficiency.
Each development strategy has advantages and disadvan-
tages, as mentioned in table 2. For example, Al-based pre-
dictive analytics can effectively improve energy efficiency,
but it requires large amounts of data and can be complex to
implement [52]. Integration of real-world data can improve
the accuracy of control models, but it can be challenging to
collect and process large amounts of real-world data [57].
Advanced electronics technology can lead to more efficient
and powerful ECUs, but it can be expensive to develop and
implement [51]. Integrated ECUs can potentially improve
EVs’ performance, efficiency, and safety. Still, they are facing
a few challenges mentioned in this section, which can be ad-
dressed for future research to improve EV energy efficiency.
E. CONTROL ALGORITHMS OF INTEGRATED ECU FOR
MOBILITY ENERGY EFFICIENCY
In an EV, the integrated ECU refers to the central electronic
control unit responsible for managing and coordinating var-
ious electrical systems and components within the vehicle.
It serves as the "brain" of the EV, overseeing the operation
of critical functions and ensuring their proper integration.
An ECU is an embedded system in EVs that controls one
or multiple systems or subsystems within a vehicle. In mod-
ern vehicles, numerous ECUs are utilized, including but not
limited to the powertrain control unit (PCU), transmission
control unit (TCU), engine control unit (ECU), brake control
module (BCM), suspension control unit (SCU), and body
control unit (BCU). [58].
Furthermore, Figure 6 shows the sub-division of ECU,
which is required to be controlled in EV for energy manage-
ment.
FIGURE 6. Taxonomy of the control algorithms of integrated ECU
III. DEVELOPMENT STRATEGIES AND ALGORITHMS OF
VCU FOR MOBILITY ENERGY EFFICIENCY
A. VCU CONFIGURATION AND SENSORS
In an autonomous vehicle, the VCU has a critical role in con-
trolling various aspects of the vehicle’s operation. To achieve
this, the VCU relies on a range of sensors, enabling it to make
informed decisions and optimize the vehicle’s performance.
Several key sensor types used in the VCU of autonomous
vehicles include powertrain control sensors, battery manage-
ment system sensors, regenerative braking control sensors,
and thermal management system sensors [59], as shown in
Figure 7.
Powertrain control sensors, such as the throttle position
sensor, measure the position of the accelerator pedal and
provide input to the VCU regarding the desired power output
from the electric motor. These sensors allow the VCU to
regulate the powertrain system and optimize its efficiency and
performance [60].
Furthermore, Battery management system sensors are cru-
cial for monitoring the battery pack’s state. They provide
data on parameters like the State of Health (SoH), temper-
ature, and State of Charge (SoC). By analyzing this data, the
VCU can ensure optimal battery performance, manage energy
consumption, and protect the battery from overcharging or
overheating [61].
In addition, Regenerative braking control sensors, includ-
ing wheel speed sensors and brake pressure sensors, enable
the VCU to engage regenerative braking, which converts the
kinetic energy into electrical energy for energy storage in
the battery. These sensors provide critical input for the VCU
to adjust the regenerative braking force and optimize energy
recovery. Thermal management system sensors monitor the
temperature of various components in the vehicle, such as
the battery pack, motor, and power electronics. These sensors
provide feedback to the VCU, enabling it to implement appro-
VOLUME 11, 2023 7
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598
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TABLE 2. Latest Research on Development Strategies of Integrated ECU in EV
Ref. Development
Strategy
Description Merits Demerits
[52]
AI-based predictive
analytic approach
Use of AI technology to en-
hance decision-making and en-
ergy efficiency in integrated
control of electric/hybrid vehi-
cles while also addressing cy-
bersecurity vulnerabilities
Sustainability: Encourages the usage of
electric/hybrid vehicles to mitigate climate
change and promotes sustainable transporta-
tion
Efficiency: AI-based predictive analysis can
improve the energy efficiency, emissions re-
duction, and general sustainability of elec-
tric/hybrid vehicles
Security: Use of AI technology to address
cybersecurity flaws in electric/hybrid vehi-
cles
Data Privacy: Large data sets fre-
quently used in predictive analytics must
be protective to mitigate privacy issues
and legal consequences.
[53]
Machine Learning Al-
gorithms:
K-Means,
Logistic Regression,
XG-Boost
Combined real data from
the ECU of an EV with
machine learning algorithms
to assess driving patterns,
enhance safety, and save fuel
consumption.
Efficiency Improvement: Attaining 100%
accuracy, precision, and recall over several
algorithms indicates the resilience and effi-
cacy of the suggested methodology in pre-
cisely identifying driving behavior.
Real-World Applicability: By utilizing ac-
tual data from a test car, the results are more
applicable to real-world driving situations
and are, therefore, more useful and relevant.
Over-fitting Risk: Over-fitting preven-
tion strategies may be the problem
while implementing such ML tech-
niques. Inadequate regularization tech-
niques might cause machine learning
models to learn the training data by heart
rather than the underlying patterns, re-
sulting in poor generalization on unob-
served data.
[54]
Integrated thermal
management system
In-depth experimental
measurements are carried out
to investigate the integration
of thermal flows. Two heat
management systems were
tested at different temperatures
and real driving emission
cycles.
Integration of Real-World Data: To ensure
accurate model development and validation,
the study incorporates a large amount of ex-
perimental data from an Internal Combus-
tion Engine (ICE). Incorporating real-world
data improves the simulation results’ accu-
racy and dependability.
Novel Co-Simulation Technique: Using
Functional Mock-up Interface (FMI), the re-
search presents an innovative approach to co-
simulation that integrates high-fidelity ICE,
thermohydraulic, and battery models.
Limited Relevance Scope: Subsequent
investigations may examine the cus-
tomization of integrated thermal man-
agement systems for other platforms,
such as plug-in hybrids and completely
electric cars, to promote more extensive
progress in thermal management tech-
niques.
[55]
Advanced Power
Electronics
Technology
Analysis of Controller
Hardware-in-the-Loop
(CHIL) simulations in EV
powertrains with a focus on
power electronics technologies
Emphasis on modern Technology: To en-
sure simulation accuracy, emphasis was
placed on modern power electronics tech-
nologies, such as field-programmable gate
arrays (FPGAs).
Prospects for the Future: EV powertrain
control innovation is being encouraged by
presenting future directions, including model
refinement and integration with Digital Twin
configurations.
Energy Efficiency Evaluation: This
study does not analyze The use of mod-
ern power electronics devices in electric
vehicle powertrains to boost energy effi-
ciency.
[56]
Regenerative braking-
based energy recov-
ery management con-
trol strategy
An innovative EV regenerative
braking system maximizes en-
ergy recovery by utilizing a
driver model to predict driving
behavior.
Efficient Energy Optimization: Improved
energy recovery efficiency, especially for ag-
gressive drivers using advanced IDP (Itera-
tive dynamic programming)-BLSTM (Bidi-
rectional Long Short Term Memory) control
approach
Limited Adaptability to Diverse Fac-
tors: Various factors (battery state, tem-
perature, and brake system health) that
affect the efficiency of the regenerative
braking system can also be included
priate thermal management strategies, including cooling or
heating, to maintain optimal operating temperatures and pre-
vent overheating [62]. All these sensors are controlled by the
VCU and coordinated by the Electronic Control Unit (ECU),
which serves as the central control hub. The ECU processes
data from these sensors and inputs from other subsystems to
make decisions and send control commands to various vehicle
components.
B. DEVELOPMENT STRATEGIES OF VCU FOR MOBILITY
ENERGY EFFICIENCY
The development strategies of VCU focus on improving the
vehicle’s performance while reducing energy consumption.
FIGURE 7. Sensors for vehicle control unit
8VOLUME 11, 2023
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This is achieved through advanced strategies that optimize
the vehicle’s systems operation. For example, the VCU can
be designed to optimize the regenerative braking system to
recover energy during braking [63].
FIGURE 8. Taxonomy of the development strategies for VCU
Development Strategies for VCU to improve mobility en-
ergy efficiency are illustrated in Figure 8. Integrating ad-
vanced sensors is crucial to enhance the capabilities of
the VCU. These sensors include position, wheel speed, ac-
celerometer, gyroscopes, and other environmental and vehicle
condition sensors. The VCU relies on accurate and real-time
data from these sensors to make informed decisions regarding
powertrain control, energy management, and vehicle stability
[64]. Efficient powertrain control is also required to maximize
the performance and range of EVs. The VCU should optimize
powertrain components such as the electric motor, battery,
inverter, and transmission. Development strategies involve
fine-tuning control algorithms to manage torque distribu-
tion, regenerative braking, energy recuperation, and thermal
management. This ensures smooth power delivery, improved
energy efficiency, and optimal utilization of the electric pow-
ertrain system [65]. Effective energy management is critical
for electric vehicles. The VCU should incorporate intelligent
algorithms that monitor the state of charge, battery temper-
ature, and power demand. By dynamically adjusting power
distribution, the VCU can optimize energy usage and balance
the power demands of various vehicle systems.
Additionally, the VCU can facilitate features like predictive
range estimation, route planning, and energy-saving modes
to increase the overall efficiency of EVs [66]. The VCU con-
tributes to ensuring the safety and stability of EVs. Develop-
ment strategies involve integrating advanced stability control
systems, like Electronic Stability Control (ESC), traction con-
trol, anti-lock braking systems (ABS), and torque vectoring.
The VCU should employ sophisticated control algorithms
that continuously monitor vehicle dynamics and make neces-
sary adjustments to prevent loss of control, improve stability,
and enhance overall driving safety [67].
Table 3 thoroughly reviews current research on VCU’s
development strategies in EVs. These strategies include many
innovative techniques and deal with multiple aspects of
VCU development. An EV transportation system that uses
blockchain and machine learning technologies to improve
data security is presented in the research by [68]. However,
there is room for future development in fine-tuning the system
for better response time and overall performance. A two-
step technique for accurate EV powertrain optimization is
presented in [65], greatly reducing computation time. The
article may, however, benefit from emphasizing the energy
efficiency gains made possible by well-optimized engine
components. Additionally, a new co-simulation method is
demonstrated by the study presented in [69], which carries
out extensive experimental measurements to explore the in-
tegration of thermal fluxes. By developing integrated thermal
management systems specifically for different car platforms,
the research’s applicability might be increased, and a more
thorough comprehension of thermal management strategies
could be encouraged. Lastly, the examination presented in
[70] concentrates on EV powertrain power electronics tech-
nology. The influence of power electronics devices on energy
efficiency in EV powertrains is not quantitatively evaluated
in the study, although it highlights current technology and
opportunities for future research paths. These critical assess-
ments underscore opportunities for refinement and further
exploration of VCU strategies in EVs.
C. VCU ALGORITHMS FOR MOBILITY ENERGY EFFICIENCY
The VCU is a control unit that controls various functions of
EVs, including steering, acceleration, and braking control.
The VCU is designed to optimize the performance of EVs
while also ensuring the safety of the passengers [71]. The
VCU can be designed to control the operation of various
powertrain components (engine, transmission, electric motor)
to reduce energy consumption. This can be achieved through
optimal gear selection, intelligent engine starts/stop systems,
and regenerative braking [23]. The VCU can be designed to
control the battery pack that powers the vehicle. It manages
battery charging and discharging and monitors its temperature
and state of charge (SoC) while ensuring battery safety and
efficiency [43]. VCU can be designed to capture and store
energy that would otherwise be wasted (e.g., through regen-
erative braking or exhaust gas heat recovery) and use it to
power auxiliary systems, reducing the load on the primary
power source [72]. VCU can also be designed to ensure the
primary goal of EV thermal management is to control the
temperatures of the battery, motor, and crew cabin to ensure
that all are working at optimal temperatures. The vehicle’s
capacity for heat dissipation is coordinated and controlled
by a thermal management system [73]. Table 4 highlights
the control algorithms, strength, and limitation of VCU for
VOLUME 11, 2023 9
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
TABLE 3. Latest Research on Development Strategies of VCU in EV
Ref. Development
Strategy
Description Merits Demerits
[68]
Sensor data collection
and secure processing
Developed an effective EV
transit system by utilizing
blockchain and machine
learning technology
Data Security: Utilized cutting-edge tech-
nology (Blockchain, Machine Learning) to
improve security and privacy while exchang-
ing data about EVs.
Response Time Improvement: Refine-
ment and optimization are needed to re-
duce latency and improve overall system
performance.
[65]
Precise and effective
EV powertrain opti-
mization
Developed a two-step analyti-
cal methodology for optimizing
EV powertrains. The first stage
examined motor features and
efficiency mapping, while the
second stage used motor data to
assess vehicle performance.
Computational Efficiency: Considerable
reduction in calculation time (approximately
250 times quicker) by the use of surrogate
models in multi-objective optimization
Energy Efficiency Improvement: Op-
timized powertrain components result
in noticeable increases in energy effi-
ciency, which is not highlighted in the
paper and can be explored in the future.
[69]
Energy management In-depth experimental
measurements are carried out
to investigate the integration
of thermal flows. Two heat
management systems were
tested at different temperatures
and real driving emission
cycles.
Integration of Real-World Data: To ensure
accurate model development and validation,
the study incorporates a large amount of ex-
perimental data from an Internal Combus-
tion Engine (ICE). Incorporating real-world
data improves the simulation results’ accu-
racy and dependability.
Novel Co-Simulation Technique: Using
Functional Mock-up Interface (FMI), the re-
search presents an innovative approach to co-
simulation that integrates high-fidelity ICE,
thermohydraulic, and battery models.
Limited Relevance Scope: Subsequent
investigations may examine the cus-
tomization of integrated thermal man-
agement systems for other platforms,
such as plug-in hybrids and completely
electric cars, to promote more extensive
progress in thermal management tech-
niques.
[70]
Stability Analysis of Controller
Hardware-in-the-Loop
(CHIL) simulations in EV
powertrains with a focus on
power electronics technologies
Emphasis on modern Technology: To en-
sure simulation accuracy, emphasis was
placed on modern power electronics tech-
nologies, such as field-programmable gate
arrays (FPGAs).
Prospects for the Future: EV powertrain
control innovation is being encouraged by
presenting future directions, including model
refinement and integration with Digital Twin
configurations.
Energy Efficiency Evaluation: This
study does not analyze The use of mod-
ern power electronics devices in electric
vehicle powertrains to boost energy effi-
ciency.
mobility energy efficiency in EVs.
IV. DEVELOPMENT STRATEGIES AND ALGORITHMS OF
EPS FOR MOBILITY ENERGY EFFICIENCY
A. EPS CONFIGURATION AND SENSORS
In EVs, precise and reliable steering control is paramount for
safe and accurate navigation. Steering control sensors are cru-
cial in providing essential input to the autonomous vehicle’s
control system [84]. Two key sensors involved in steering
control are the torque sensor and steering angle sensor, which
work in conjunction with the Electrical Power Steering (EPS)
system, ultimately controlled by the Electronic Control Unit
(ECU) as illustrated in Figure 9.
The steering angle sensor measures the rotational position
of the steering wheel, providing real-time information on
the driver’s intended direction. It enables the autonomous
vehicle’s control system to accurately interpret the driver’s
steering inputs and translate them into appropriate commands
for the steering actuator [85]. On the other hand, the torque
sensor measures the level of torque applied to the steering
column. It detects the driver’s effort to turn the steering wheel,
providing critical feedback on the driver’s intentions and the
resistance encountered while maneuvering the vehicle. The
control system utilizes This torque information to enhance
steering control precision and implement advanced driver as-
sistance features, such as torque-based steering interventions
and lane-keeping assistance [86].
Furthermore, the steering angle and torque sensors are in-
tegrated into the EPS system, replacing traditional hydraulic
power steering with an electrically assisted mechanism. The
EPS system utilizes electrical power, controlled by the ECU,
to assist the driver’s steering inputs and deliver the appropriate
steering response. The ECU processes the steering angle and
torque sensor data, applying sophisticated control algorithms
to regulate the EPS system’s behavior and provide the desired
steering assistance [87]. Integrating these sensors and the EPS
system into the overall control architecture of autonomous
vehicles enables precise and dynamic steering control, allow-
ing for smooth maneuvering and accurate trajectory tracking.
The ECU, the central control unit, coordinates the interactions
between the sensors, EPS system, and other control modules
to achieve safe and reliable autonomous steering [88].
B. DEVELOPMENT STRATEGIES OF EPS FOR MOBILITY
ENERGY EFFICIENCY
Figure 10 illustrates the main development strategies for
EPS to improve mobility energy efficiency. The develop-
ment strategies for EPS in electric vehicles for mobility
energy efficiency revolve around efficient power assistance
control, regenerative energy harvesting, intelligent steering
10 VOLUME 11, 2023
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TABLE 4. Control Algorithms of vehicle control unit in electric vehicles
Ref. Control Algorithm Description Strength Limitation
[74],
[75]
Proportional-
Integral-Derivative
(PID) Control
PID control is a commonly used algorithm that adjusts
the control signal based on the error between the de-
sired and actual states of the vehicle, helping maintain
stability and accuracy.
Easy Implementation, low
Computational cost, work well
for linear systems
Parameter tuning is required
for optimal performance, lim-
ited adaption for systems with
varying dynamics
[76],
[77]
Model Predictive
Control (MPC)
MPC is an advanced control algorithm that uses a
dynamic system model to optimize control inputs. It
considers predictions of future states and constraints
to determine the optimal control action.
Optimal control based on fu-
ture prediction, handle multiple
constraints at a time
High computational cost, sensi-
tive to model inaccuracies
[77],
[78]
Field-Oriented Con-
trol (FOC)
FOC is a motor control algorithm that enables precise
control of torque and flux in an electric motor. It
decouples the torque and flux components, allowing
independent control and efficient utilization of the
motor’s capabilities
Accurate torque and speed con-
trol. It enhances motor per-
formance by reducing motor
losses
Accurate motor parameters set-
ting required, vulnerable to
varying parameters and noise
from sensor.
[79],
[80],
[81]
Direct Torque Con-
trol (DTC)
TDTC is a control algorithm that directly regulates
torque and flux in an electric motor without calcu-
lating or controlling the motor currents. It offers fast
torque response and precise control by selecting the
optimal voltage vectors
Simple implementation com-
pared to FOC, fast torque re-
sponse with fewer ripples
Switching frequency is
increased, sensitivity to
parameter variations.
[82],
[83]
Adaptive Control Adaptive control algorithms adjust the control param-
eters based on real-time changes in system dynamics.
These algorithms continuously estimate and update
the model parameters to accommodate variations in
vehicle and environmental conditions
Robust control in varying op-
erating conditions, adaptable to
different vehicles and driving
scenarios
Precise parameter tuning is re-
quired, limitations for highly
non-linear systems.
FIGURE 9. Overview model of the steering control based on different
installed sensors
assistance, variable assistance levels, vehicle dynamics con-
trol systems integration, lightweight component design, and
advanced sensor integration. Implementing these strategies
can optimize the EPS system’s performance, minimize energy
consumption, and contribute to the overall energy efficiency
of electric vehicles.
Furthermore, EPS systems are more energy-efficient than
traditional hydraulic power steering systems. The electric
motor in an EPS system only consumes power when steering
assistance is required, whereas hydraulic systems consume
power continuously [89]. High-efficiency electric motors can
be used in EPS systems to reduce energy consumption. These
motors have higher efficiency levels, resulting in lower en-
ergy losses during operation [90]. Another effective strategy
for enhancing mobility energy efficiency is the implementa-
tion of regenerative energy harvesting in EPS systems. By
utilizing regenerative braking technology, the EPS system can
FIGURE 10. Taxonomy of the development strategies for EPS
recover energy during braking or deceleration and convert
it into electrical energy that can be stored in the vehicle’s
battery. This harvested energy can then be utilized to power
other vehicle systems, reducing the overall energy consump-
tion of the electric vehicle [91]. Integrating intelligent steer-
ing assistance capabilities in EPS systems can significantly
improve mobility energy efficiency. This involves developing
algorithms that adaptively adjust the steering assistance based
on various factors, such as road conditions, vehicle speed,
and driver behavior. The EPS system can minimize energy
VOLUME 11, 2023 11
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consumption and optimize steering control by providing the
right assistance at the right time, resulting in improved energy
efficiency [92]. Efforts to enhance mobility energy efficiency
in electric vehicles should consider lightweight component
design for EPS systems. Lightweight materials and efficient
design can help reduce the overall mass of the EPS compo-
nents, thereby reducing energy requirements for operation.
Optimizing the mechanical design, such as reducing friction
and improving bearing efficiency, can further contribute to
improved energy efficiency [93].
An overview of current research on EPS system devel-
opment strategies in EVs is given in Table 5. Although the
table provides insightful information on novel strategies for
improving torque output and energy efficiency in EPS, a few
important points need to be addressed. Firstly, questions con-
cerning the Differential Drive Collaborative Steering (DDCS)
control system’s adaptability to a range of working situations
and vehicle characteristics are raised by the system’s lack of
real-time optimization and performance assessment metrics
[94]. Secondly, the study concentrating on torque produc-
tion enhancement via flux-switching Permanent Magnet Ma-
chines (FSPMMs) shows encouraging results; however, the
absence of a thorough assessment regarding its improvement
in energy efficiency leaves room for additional research on
this topic [95]. Furthermore, examining the energy efficiency
of electric SUVs in real-world driving situations gets recogni-
tion for its practical methodology, even though the complexity
brought about by the variety of real-world driving scenarios
may make energy efficiency assessments more difficult [96].
Finally, a promising technological advancement is presented
by the creative application of a triboelectric nanogenerator
(TENG)-based sensor for driver intention detection in EPS
[97]. However, consistent performance of the sensor requires
addressing its sensitivity to environmental factors. These
important elements draw attention to areas that need more
research and improvement in EPS strategies for EVs.
C. EPS ALGORITHMS FOR MOBILITY ENERGY EFFICIENCY
EPS is a system that uses an electric motor to assist in steering
a vehicle. EPS is designed to improve the efficiency and
performance of the vehicle’s steering system while reducing
its energy consumption. EPS focuses on reducing the energy
consumption of the vehicle’s steering system while improving
its performance. This is achieved using advanced control
algorithms that optimize the electric motor operation [98].
For example, the EPS system can be designed to adjust the
electric motor’s level of assistance based on the vehicle’s
speed and steering angle. The EPS system can adapt the
necessary steering effort required by the driver for turning
the steering wheel. Making it easier for the driver to steer the
vehicle [99]. The EPS system can adjust the responsiveness
of the steering, making it more or less sensitive to driver
input [100]. The EPS system can adjust the steering assist
to help stabilize the vehicle in different driving conditions,
such as during high-speed driving or in crosswinds [101]. The
EPS system can also adjust the feedback the driver receives
from the steering, providing a more or less connected feeling
between the driver and the road [102]. Table 6 summarizes
the EPS control algorithms for mobility energy efficiency in
EVs, including strength and limitation.
V. DEVELOPMENT STRATEGIES AND ALGORITHMS OF
ESC FOR MOBILITY ENERGY EFFICIENCY
A. ESC CONFIGURATION AND SENSORS
Electronic Stability Control (ESC) systems in electric ve-
hicles rely on sensors to accurately determine the driver’s
intentions and monitor the vehicle’s response. These sensors
play a crucial role in ensuring the effectiveness of the ESC
system. One key aspect is using speed and steering angle
measurements to ascertain the driver’s desired heading [110].
Concurrently, onboard silicon-based sensors, manufactured
using micromachining technologies, measure the vehicle’s
lateral acceleration and yaw rate. Over the past decade,
these Micro-Electro-Mechanical Systems (MEMS) sensors
have revolutionized ESC systems by significantly enhancing
their size, cost, and reliability, surpassing traditional high-
precision mechanical sensors [111].
Furthermore, the ESC system’s control algorithm, residing
in the ESC controller, continuously receives data from the
wheel speed sensors, steering angle sensor, and yaw rate
sensor, as illustrated in Figure 11. The controller stores an
application algorithm that incorporates equations represent-
ing the vehicle’s dynamics [112]. By comparing driver inputs
to the vehicle’s response, the control algorithm determines
the need for intervention, such as applying the brakes through
hydraulic modulator valves or adjusting the throttle. When the
vehicle’s yaw rate aligns with lateral acceleration and speed,
it indicates a balanced response to the steering input [113].
In addition, the ESC controller can exchange data with
other controllers in connection with the vehicle network (con-
troller area network). This capability enables further enhance-
ments to the stability and control of EV [61]. Additionally,
ESC systems typically offer an option to disable the function,
which proves beneficial in specific off-road driving condi-
tions or when using a smaller spare tire that may interfere
with the sensors. Some vehicle manufacturers even provide
an additional mode that allows drivers to explore the limits
of tire grip with reduced electronic intervention. However,
the ESC system automatically returns to normal operation
upon ignition restart, ensuring continued safety and stability
control [114].
B. DEVELOPMENT STRATEGIES OF ESC FOR MOBILITY
ENERGY EFFICIENCY
The development strategies for ESC in electric vehicles are
similar to those in traditional combustion engine vehicles.
The system typically uses sensors to monitor various vehicle
parameters, including speed, acceleration, steering angle, and
wheel rotation. Using this information, the ESC control unit
can detect when a loss of traction is about to happen and
intervene by reducing engine power or applying individual
brakes to specific wheels. However, in an electric vehicle,
12 VOLUME 11, 2023
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598
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TABLE 5. Latest Research on Development Strategies of EPS in EV
Ref. Development
Strategy
Description Merits Demerits
[94]
Differential Drive
Collaborative
Steering (DDCS)
control system
Designed a DDCS optimal con-
trol approach with an energy-
saving mechanism that priori-
tizes reducing steering energy
usage.
Energy Efficiency Enhancement: The in-
novative DDCS technology offers an excep-
tional way to save energy in EVs with artic-
ulated steering.
Real-Time Optimization: Absence of
real-time optimization for performance
assessment under diverse working con-
ditions and vehicle parameters.
[95]
Enhancement of
torque production
by FSPMMs (Flux-
switching permanent
magnet machines) in
EPS system
Proposed a strategy to improve
torque generation that neutral-
izes negative torque contribu-
tions by inserting harmonic
currents.
Enhanced Torque Output: By optimizing
harmonic current parameters, a 9% increase
in mean torque was obtained.
Energy Efficiency Evaluation: Lack of
conclusive energy efficiency improve-
ment opens room for future research.
[96]
Electric SUV’s (Sport
Utility Vehicle)
energy efficiency
under real-world
driving conditions
Determined the effect of driv-
ing speed and the phases of re-
generative braking on energy
consumption
Real-World Analysis: Analyzing energy ef-
ficiency under various driving scenarios of-
fers insights into actual performance.
Complex Real-World Impact:Energy
efficiency evaluation is complicated by
real-world driving conditions
[97]
Intelligent steering
wheel utilizing
a triboelectric
nanogenerator
(TENG)-based sensor
Developed a TENG-based sen-
sor incorporated into the steer-
ing wheel to determine the
driver’s intentions.
Innovative Sensor Technology: Proposed a
new TENG-based sensor with quicker re-
sponse time and efficient driver intention de-
tection
Sensitivity to Environmental Factors:
Environmental factors may impact the
sensor’s efficacy in different situations.
TABLE 6. Control Algorithms of Electric Power Steering in Electric Vehicles
Ref. Control Algorithm Description Strength Limitation
[103],
[88]
Torque Overlay
Control
Torque overlay control is a basic EPS algorithm that
adds an electric motor-generated torque to the driver’s
steering input to assist the driver in steering maneuvers
and provides adjustable steering effort
Improved driver comfort by
providing adjustable steering
effort, easy implementation and
cost-effective
Limited usefulness in com-
plex driving situations, depends
upon inputs from driver for pre-
cise control
[104],
[98]
Active Front Steer-
ing
AFS is a control algorithm that modulates the steering
angle of the front wheels in response to various vehicle
and sensor inputs by adjusting the front wheel steering
angle
Enhances maneuverability dur-
ing high-speed or emergency
maneuvers
Certain limitations persist in
extreme driving scenarios, in-
crease complexity of steering
system.
[105],
[106]
Active Return Con-
trol
Active return control is an EPS algorithm that auto-
matically assists the driver in returning the steering
wheel to its neutral position after completing a turn
Improves vehicle stability, pro-
vides smoother steering inputs
Highly reliable response from
actuators, more complex EPS
system with higher cost.
[107],
[108]
Variable Gear Ratio
Control
Variable gear ratio control adjusts the steering gear
ratio based on various parameters, such as vehicle
speed, steering angle, and driving conditions
Improves vehicle stability at
high speeds, optimizes steering
characteristics
Requires additional mechanical
components, increases
complexity and cost
[109] Active Damping
Control
Active damping control is an advanced EPS algorithm
that adjusts the damping characteristics of the steering
system in real-time
Improves vehicle stability with
ride comfort, efficiently adjust-
ing damping force based on
changing road conditions
Highly reliable on actuators
and sensors, increases system
complexity and cost
FIGURE 11. Overview of the stability control using different installed
sensors [115]
there may be some differences in the design strategy for
ESC. For example, because electric motors can deliver instant
torque, ESC in an EV may need to respond even more quickly
than in a traditional vehicle. Additionally, some EVs may
use regenerative braking, which can complicate the control
strategy for ESC, as the system needs to balance the regen-
erative braking force with the conventional friction brakes to
maintain stability [116].
Furthermore, Figure 12 illustrates the main development
strategies for ESC to improve mobility energy efficiency.
ESC can be designed to encourage smoother driving by
reducing unnecessary acceleration and deceleration. By min-
imizing sudden speed changes, the vehicle can maintain a
more consistent energy output, which can help improve ef-
ficiency and extend the battery’s range [117]. The vehicle’s
design, including the placement of the battery and electric
VOLUME 11, 2023 13
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
FIGURE 12. Taxonomy of the development strategies for ESC
motor, can affect the stability of the vehicle and its energy
efficiency. By optimizing the weight distribution, ESC can
help ensure that the vehicle stays stable and balanced, reduc-
ing the need for corrective maneuvers that can waste energy
[118]. ESC can be designed to handle sensor fusion, which
involves combining data from multiple sensors such as LI-
DAR (light detection and ranging), RADAR (Radio detection
and ranging), wheel speed sensors, steering angle sensors,
and gyroscope, etc., to integrate data for real-time monitoring
resulting in enhanced accuracy [119].
A thorough summary of current research on ESC system
development techniques in EVs can be found in Table 7.
Although the table presents novel strategies to improve the
effectiveness and functionality of ESC, there are significant
factors that require taking into account. Firstly, handling
stability and motor efficiency are significantly improved in
the study using a torque distribution strategy based on Deep
Reinforcement Learning (DRL) [120]. Future improvements
will likely be necessary because of the computing demands
of large-scale implementations, which might provide difficul-
ties. Secondly, it is noteworthy that Phase Change Material
(PCM) has been included in Battery Thermal Management
Systems (BTMS) to improve thermal efficiency [121]. How-
ever, long-term dependability issues with PCM deterioration
and ideal material combinations indicate areas that need more
investigation. Lastly, to maximize energy efficiency without
sacrificing perception performance, Adaptive Sensor Fusion
with Energy Awareness for object detection in autonomous
vehicles (AVs) presents a potential method [122]. However,
given the model’s restricted application to particular settings,
more research should be conducted using comparative assess-
ments and thorough data-driven analysis. These important
considerations highlight areas where ESC techniques for EVs
should be worked upon to advance the research in this field
for further energy efficiency improvement.
C. ESC ALGORITHMS FOR MOBILITY ENERGY EFFICIENCY
ESC is a safety system in EVs designed to help drivers
maintain control of their vehicles during emergencies or in
hazardous road conditions [123]. ESC works by detecting and
reducing instances of oversteer or understeer, which occur
when a vehicle loses traction with the road surface, caus-
ing it to spin or slide out of control. In an electric vehicle,
ESC can also play a role in improving energy efficiency
and range [124]. By monitoring the vehicle’s stability and
reducing unnecessary acceleration or deceleration, ESC can
help prevent wasteful energy consumption and extend the
battery’s range. To provide stability, the ESC monitors the
yaw rate and steering wheel sensors to detect a lack of steering
control. Once detected, the ESC activates the ABS (Antilock
Braking System) and ECM (Engine control module) to reduce
the vehicle’s speed. The ESC system can detect when one or
more wheels are slipping and apply the brakes or adjust the
power to the motor to help maintain traction and prevent the
vehicle from skidding or spinning out of control. The ESC
system can detect when the vehicle is starting to oversteer or
understeer and apply the brakes or power adjustment of the
motor to help keep the vehicle on the intended path [125]. The
ESC system can detect when the vehicle is at risk of rolling
over and apply the brakes or adjust the power supply to the
motor to help prevent the vehicle from rolling over [126]. The
ESC system can also hold the brakes for a short period when
the vehicle is stopped on a hill, allowing the driver to start
the vehicle without rolling backward [127]. Table 8 shows
the ESC control algorithms for mobility energy efficiency in
EVs along with their strength and limitations.
VI. DEVELOPMENT STRATEGIES AND ALGORITHMS OF
BCU FOR MOBILITY ENERGY EFFICIENCY
A. BCU CONFIGURATION AND SENSORS
The body control unit (BCU) in electric vehicles incorpo-
rates various sensors to monitor and control different systems
within the vehicle’s body, as shown in Figure 13. These sen-
sors enhance the occupants’ safety, convenience, and comfort.
Some of the key sensors used in the BCU of electric vehicles
include those for lighting control, windshield wipers, power
windows, door locks, and the HVAC (Heating, Ventilation,
and Air Conditioning) system [135].
Furthermore, lighting sensors enable the automatic control
of lighting systems, such as automatic headlights and interior
lighting. These sensors detect ambient light levels and adjust
the vehicle’s lighting accordingly, enhancing visibility and
reducing driver distraction. Windshield wiper sensors detect
rain or moisture on the windshield and activate the wipers
automatically. These sensors ensure optimal visibility for the
driver, enhancing safety during inclement weather conditions.
Power window sensors detect obstacles in the window’s path,
preventing injuries and damage. They automatically stop the
window movement if an obstruction is detected, ensuring the
safe operation of the power windows [136].
Similarly, door lock sensors are vital to the vehicle’s se-
curity. They detect whether the doors are open or closed and
14 VOLUME 11, 2023
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598
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TABLE 7. Latest Research on Development Strategies of ESC in EV
Ref. Development
Strategy
Description Merits Demerits
[120]
Torque Distribution
Strategy Based on
Deep Reinforcement
Learning (DRL)
DRL-based approach for
torque distribution in EVs.
Torque distribution is a
decision-making process that
incorporates motor efficiency
in cumulative reward.
Better Handling Stability: The proposed
strategy significantly lowers energy loss in
the range of 5.25% to 10.51% during typical
steering motions and greatly increases aver-
age motor efficiency.
Large-scale computational capability:
Optimization of algorithms can be done
in the future for more effective compu-
tation
[121]
Phase change material
(PCM) assisted bat-
tery thermal manage-
ment system (BTMS)
The study proposed the integra-
tion of jute fibers with PCM-
based cooling techniques to in-
crease the thermal efficiency of
BTMS in EVs
Enhanced Thermal Efficiency: Compared
to conventional cooling techniques, the pro-
posed novel strategy produces noticeably
lower maximum temperatures, indicating im-
proved thermal efficiency
Long-term Reliability: Interior perfor-
mance of PCM, degradation of jute
fibers, and determining the optimal
number of jute layers can be studied as
the future work for developing robust
and durable BTMS
[122]
Adaptive Sensor
Fusion with Energy
Awareness for
object detection in
autonomous vehicles
(AVs)
The proposed method opti-
mizes energy usage without
sacrificing perception perfor-
mance by adapting the fusion
mechanism based on circum-
stances.
Improved Efficiency with Reduced En-
ergy Utilization: The suggested approach
performs up to 9.5% better than the current
sensor fusion techniques while using around
60% less energy and reducing latency by
58%.
Restricted Applicability: The study
concentrates on particular scenarios or
circumstances. To improve the flexibil-
ity of the proposed model, future work
could involve comparative evaluations
and data-driven analysis
TABLE 8. Control Algorithms of Electronic Stability Control in Electric Vehicles
Ref. Control Algorithm Description Strength Limitation
[128],
[129]
Anti-lock Braking
System (ABS)
Control
ABS control is a fundamental component of ESC sys-
tems that modulates the braking force at each wheel
to prevent wheel lock-up during braking
Prevents wheel lock-up during
braking, improves stability and
control of EV
Low capability to address lat-
eral stability issues, does not
actively involve in vehicle yaw
motion
[130],
[131]
Traction Control
System (TCS)
Control
TCS control is designed to prevent wheel slip during
acceleration by utilizing wheel speed sensors to mon-
itor individual wheel speeds and modulates the power
or torque delivered to the wheels to optimize traction
Improve the traction and stabil-
ity control during acceleration,
avoid excessive wheel spin and
loss of control
Focuses primarily on accelera-
tion and traction, not lateral sta-
bility, limited control over yaw
motion
[132],
[133]
Roll Stability Con-
trol (RSC)
Active return control is an EPS algorithm that auto-
matically assists the driver in returning the steering
wheel to its neutral position after completing a turn
Improves vehicle stability, pro-
vides smoother steering inputs
Highly reliable response from
actuators, more complex EPS
system with higher cost.
[134] Hill Start Assist
Control (HSAC)
HSAC is an ESC algorithm that prevents a vehicle
from rolling backward when starting on an incline by
holding the brake pressure for a short period, allowing
the driver to smoothly transition from the brake pedal
to the accelerator without the vehicle rolling backward
Enhances driver convenience
and control, reduces the like-
lihood of accidents during hill
starts
Limited functionality specifi-
cally for hill start situations, re-
quires accurate detection and
control of the slope gradient.
provide feedback to the BCU. This information is used for
various functions, such as automatically locking the doors
when the vehicle is in motion or unlocking the doors when
the vehicle is parked. The HVAC system sensors monitor
parameters like temperature, humidity, and cabin occupancy.
They enable the BCU to control the HVAC system effectively,
maintaining a comfortable environment inside the vehicle.
These sensors work with the BCU to provide intelligent con-
trol over various body systems in electric vehicles, ensuring
enhanced safety, convenience, and comfort for the occupants.
B. DEVELOPMENT STRATEGIES OF BCU FOR MOBILITY
ENERGY EFFICIENCY
Different development strategies are presented in Figure 14
for a BCU to improve mobility energy efficiency. These
strategies include power management, regenerative braking,
lightweight design, and V2V communication. The BCU can
manage the power consumption of various electronic systems
in the vehicle by turning them off when not in use. For
FIGURE 13. Basic configuration of BCU using input sensors and output
controls
example, the BCU can turn off the climate control system
when the vehicle is not occupied [137]. Advanced thermal
control systems can be designed to guarantee that the BCU
operates within ideal temperature ranges, hence lowering
VOLUME 11, 2023 15
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598
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the requirement for energy-intensive cooling systems and
reducing the load on the electrical system by utilizing the
thermal energy produced within the car for heating. [73]. The
BCU can be designed to communicate with other vehicles
on the road. This can allow for more efficient driving by
sharing information about traffic conditions and optimizing
the operation of electronic systems in the vehicle [138].
Table 9 comprehensively describes current research on
the development approaches used for BCUs in EVs. These
strategies demonstrate creative methods for improving energy
efficiency and maximizing vehicle performance. The study
introducing the Long Short-Term memory-based improved
Model Predictive Control algorithm (LSTM-IMPC) showed
promising results in increasing fuel efficiency, ultimately
resulting in improved energy efficiency [139]. However, it’s
crucial to remember that the algorithm solely addresses in-
ternal vehicle energy management; real-time sensor data and
significant external elements are not included, indicating a
potential area for future research. Additionally, it is notewor-
thy that Nonlinear Model Predictive Control (NMPC) is used
for cabin cooling systems since it may increase efficiency
while lowering energy consumption [140]. However, there
is still a need for more research and development into how
resistant the NMPC model is to uncertainties such as un-
modeled dynamics and disturbances. Furthermore, fuel con-
sumption is significantly reduced by the Multi-Agent Rein-
forcement Learning (MARL) based optimal energy-saving
strategy for Hybrid Electric Vehicles (HEVs) presented by
[141]. Nonetheless, optimization and practical application
issues may impact the method’s adaptability, which can be
considered for future research. These considerations under-
score possible paths for future study in BCU techniques in
EVs and emphasize improving these strategies to guarantee
their efficacy in real-world circumstances.
FIGURE 14. Taxonomy of development strategies for BCU
C. BCU ALGORITHMS FOR MOBILITY ENERGY EFFICIENCY
The body control unit (BCU) is a component in electric vehi-
cles (EVs) that controls the various electronic systems in the
vehicle, including lighting, climate control, and the locking
system. By optimizing the operation of these systems, the
BCU can improve the vehicle’s energy efficiency. The BCU
controls the exterior and interior lighting systems, including
headlights, taillights, brake lights, turn signals, and interior
lights [142]. The BCU controls the windshield wipers, includ-
ing the speed and frequency of the wipers. The BCU controls
the power windows’ operation, including the windows’ open-
ing and closing. The BCU controls the operation of the door
locks, including the locking and unlocking of the doors [135].
Besides, the BCU controls the heating, ventilation, and air
conditioning (HVAC) system, including the temperature, fan
speed, and airflow [143]. Table 10 illustrates the BCU control
algorithms for mobility energy efficiency in EVs, mentioning
their strength and limitations.
VII. COMPARISON OF INTEGRATED ECU ALGORITHMS
FOR MOBILITY ENERGY EFFICIENCY
Table 11 presents a critical analysis of existing control ap-
proaches for energy efficiency in EVs using regenerative
braking. Different factors are considered to analyze existing
control strategies to highlight the research gap critically. The
factors incorporate the type of control method used, the main
objective of the proposed study, strengths, and limitations.
In [148], Adeleke et al. presented an in-depth torque dis-
tribution approach for four in-wheel motor drive EVs. With
an emphasis on energy efficiency, they use the dynamic pro-
gramming (DP) technique to optimize the torque distribution
between the front and rear in-wheel motors. The suggested
DP algorithm has been shown through thorough modeling and
experimental research to dramatically reduce the vehicle’s
energy consumption across various driving cycle situations,
demonstrating notable increases in energy efficiency. It’s
important to remember that DP necessitates more time and
space due to its comprehensive computations. Future areas
for research need to concentrate on refining the method to
augment computational effectiveness while maintaining the
capacity for real-time monitoring.
In [149], Lee et al. proposed a model to reduce energy
consumption in autonomous EVs by optimizing speed in
various driving circumstances. The model-based Reinforce-
ment Learning (RL) method is used for eco-driving. The
RL algorithm outperformed the global optimum solution by
93.8% using vehicle powertrain dynamics. Future work could
focus on experiments in the real world and more valida-
tion under various driving circumstances. Reducing algorithm
complexity and increasing convergence speed via techniques
like transfer learning is crucial for improving practical imple-
mentation, which can be explored in future studies.
In [150], Pei et al. proposed an efficiency optimiza-
tion strategy for EV motors, enhancing efficiency by 4-7%
through a novel Loss Minimization Algorithm (LMA) based
on the motor’s energy balance equation. The LMA adjusts
excitation current, improving efficiency during steady-state
operations. However, the method assumes constant motor
parameters, limiting its applicability. Real-world experiments
16 VOLUME 11, 2023
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3356598
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
TABLE 9. Latest Research on Development Strategies of BCU in EV
Ref. Development
Strategy
Description Merits Demerits
[139]
Long short-term
memory based
improved model
predictive control
algorithm (LSTM-
IMPC)
Proposed an LSTM-IMPC al-
gorithm for the energy man-
agement strategy in plug-in hy-
brid electric vehicles (PHEV)
and evaluated performance in
comparison to rule-based tech-
niques
Enhanced Fuel Efficiency: The proposed
model shows considerable fuel-saving rates
of 3.81%, 5.6%, and 18.71% over the tradi-
tional energy management systems
External Factors and Real-Time
Data: The proposed EMS addresses
internal vehicle energy management,
ignoring external variables and real-time
data from sensors which could be added
in future work
[140]
Nonlinear Model
Predictive Control
(NMPC) for Cabin
Cooling System
The proposed model ensures
thermal comfort while consum-
ing the least amount of energy
by optimizing the air mass flow
trajectories and cabin inlet air
temperature
Improved Efficiency with Reduced En-
ergy Utilization: The suggested approach
performs up to 9.5% better than the current
sensor fusion techniques while using around
60% less energy and reducing latency by
58%.
Robustness and Real-World Imple-
mentation: Robustness of the proposed
NMPC has uncertainty concerns like
unmodeled dynamics and disturbances,
which could be explored in the future to
improve the NMPC’s robustness
[141]
Multi-Agent
Reinforcement
Learning (MARL)
based optimal energy-
saving strategy for
Hybrid Electric
Vehicle (HEV)
The proposed model enables
cooperative control of the en-
gine and the car-following be-
haviors by actively reducing
energy consumption.
Significant Fuel Consumption Reduction:
The paper’s proposed MARL model demon-
strates fuel consumption reduction by 15.8%
compared to the hierarchical Model Predic-
tive Control (MPC) strategy, ensuring signif-
icant advancement in energy efficiency
Enhancing Algorithm’s Adaptability:
The suggested MARL algorithm could
face challenges in optimization and real-
world applicability, which can be im-
proved in future research
TABLE 10. Control Algorithms of Body Control Unit in Electric Vehicles
Ref. Control Algorithm Description Strength Limitation
[144] Lighting Control Lighting control algorithms in the BCU manage var-
ious lighting functions, including exterior lighting,
interior lighting, and adaptive lighting systems
Automated lighting control for
enhanced safety, optimizes en-
ergy efficiency by adjusting
lighting levels
Requires accurate sensor inputs
for optimal performance, vul-
nerable to sensor malfunction-
ing
[145] Wiper Control Wiper control algorithms in the BCU regulate the op-
eration and speed of windshield wipers based on input
from rain sensors, vehicle speed, and user settings
Automated wiper control for
improved visibility and safety,
adapt wiper operation to vary-
ing rain intensities
Relies on accurate rain sensor
inputs for optimal control, re-
quires fine-tuning for different
weather conditions
[146] Power Windows
Control
Power windows control algorithms in the BCU man-
age the operation and positioning of the vehicle’s
power windows
Precise control over
power window operation,
incorporates anti-pinch
functionality for user safety
Requires reliable sensor in-
puts for anti-pinch functional-
ity, vulnerable to motor or sen-
sor malfunctioning
[147] HVAC Control HVAC control algorithms in the BCU regulate the
operation of the Heating, Ventilation, and Air Condi-
tioning system in the vehicle
optimize energy efficiency by
adjusting HVAC parameters as
needed
rely on accurate sensor inputs
for optimal control and vulner-
able to sensor malfunctioning
and parameter variations, such as temperature, are necessary
for comprehensive validation and practical implementation in
diverse EV scenarios, which can be researched in the future.
In [151], Nassar et al. developed multi-objective energy
management strategies for pre- and post-transmission par-
allel hybrid electric vehicles (HEVs). These strategies use
genetic algorithms to optimize fuel consumption, electric
system efficiency, and battery life. Results show improved
battery performance and varied effects on fuel consumption
compared to a baseline strategy. However, the study’s lim-
ited applicability requires further validation, and real-time
implementation complexities suggest the need for adaptive
algorithms in future research.
In [152], Mehbodniya et al. investigated optimization
methodologies for three-phase induction motors, emphasiz-
ing field-oriented control and direct torque control tech-
niques. The study shows improved performance and energy
efficiency using fractional order Darwinian particle swarm
optimization (FODPSO), especially for the direct torque con-
trol method. Nevertheless, comprehensive comparison as-
sessments of different optimization strategies should be in-
cluded in the work. Future work should incorporate com-
prehensive performance comparisons and real-world appli-
cations to facilitate decision-making in the real world effec-
tively.
In [153], Xu et al. investigate deep reinforcement learning-
based energy management strategy (EMS) for hybrid electric
vehicles (HEVs). The study investigates how deep reinforce-
ment learning (DRL) approaches might be applied to trans-
fer learning. It evaluates several exploration methods inside
the Deep Deterministic Policy Gradient (DDPG) algorithm
architecture, such as introducing noise into the parameter and
action spaces. The findings show that, in comparison to the
combination of parameter space noise and action space noise,
VOLUME 11, 2023 17
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adding parameter space noise in the network is more stable
and converges faster for transferable EMS. One limitation
of the study is that it investigates only the noise addition
technique. It leaves up the potential for future research by
investigating a wider variety of exploratory approaches with
robust DRL algorithms to enhance EMS in HEVs.
In [154], Zhang et al. proposed and simulated a hierar-
chical model predictive control algorithm for the thermal
management of EV by using the energy restored through
regenerative braking. The upper layer MPC controller opti-
mally plans the vehicle’s speed, while the lower layer focuses
on implementing thermal management strategies based on
the planned regenerative braking for energy efficiency. By
conducting experiments, the proposed method demonstrates
a reduction in energy consumption by 3.38% and a decrease
in battery aging of up to 29.15% when compared to the
benchmark method. The study may have focused on a specific
vehicle or air conditioning system, and the findings may not
apply to other vehicle types or HVAC (heating, ventilation,
and air conditioning) systems. The study may have yet to
fully consider the impact of other factors, such as weather
conditions or driver behavior. The cost and complexity of
retrofitting existing vehicles with the necessary equipment to
implement the proposed strategy may be prohibitively high,
limiting its adoption.
In [155], Satzger et al. introduce a predictive braking con-
trol method for EVs that uses a predictive control framework
to optimize energy recuperation and wheel slip regulation.
The algorithm is designed for EVs with redundant braking
actuators. The algorithm was experimented on the ROboMO-
bil (ROMO) research vehicle, and results showed up to a 60
percent reduction in torque tracking error and a 10 percent
improvement in emergency deceleration compared to other
control techniques.
There could be several limitations to this study. One limi-
tation could be the specific experimental setup used for val-
idation. The ROboMObil (ROMO) research vehicle used in
the study may only represent some types of electric vehicles,
which could limit the generalizability of the findings. Addi-
tionally, the proposed algorithm’s effectiveness may depend
on specific driving conditions and scenarios not explored in
the study. Finally, the cost and complexity of implementing
the proposed algorithm in a production vehicle may also limit
its practical application.
In [156], Chen et al. proposed a hierarchical framework for
regenerative braking control in independently operated EVs.
The upper-layer sliding mode controller (SMC) estimates
vehicle states by a modular observer, which tracks desired
velocity profiles. After calculating the overall braking torque,
a lower-layer controller uses a control allocation algorithm
to split the braking torque between the front and rear wheels
as efficiently as possible to maximize energy recovery, ulti-
mately improving EV efficiency. Although useful, the study’s
dependence on certain tire characteristics for velocity esti-
mation has limitations and points to the need for adaptable
methodologies to be explored in the future.
In [157], Kousalya et al. proposed the implementation of
predictive torque control (PTC) as an energy-saving approach
for the motor used in EV. The PTC method was used to
overcome the torque ripple problem in conventional direct
torque control (DTC). The electric motor’s torque ripple and
speed response in EVs are analyzed across various operating
modes. This examination aims to generate an energy-saving
plan designed for the electric motor. The paper’s limitation
lies in the high computational burden, dependence on sys-
tem models, and challenges in tuning weighting factors for
Predictive Torque Control (PTC). Future studies could fo-
cus on developing efficient computational methods, adaptive
algorithms for changing parameters, and automated tuning
techniques to enhance PTC’s applicability in traction motors.
In [158], Zerd et al. proposed a novel speed-sensorless
finite control set-predictive torque control for EVs utiliz-
ing induction motor (IM) drives. The method combines the
advantages of IM with the ability to handle nonlinearities
and eliminates speed sensors, increasing reliability and re-
ducing costs. An adaptive fading extended Kalman filter is
introduced to approximate load torque and enhance torque
response. The proposed method shows improved control per-
formance, making it a promising solution for electric vehicle
propulsion systems. One potential limitation of the research
study is that the proposed method may have limitations in
high-speed applications due to the finite control set and
prediction errors. Moreover, the proposed method requires
an exact approximation of the load torque, which can be
challenging in some operating conditions. Additionally, the
experimental studies were performed on a small-scale motor,
and further research may be required to validate the proposed
method on larger-scale motors and under different operating
conditions. Finally, the cost and complexity associated with
implementing the adaptive fading extended Kalman filter may
be a limitation for practical applications.
In [159], Vajedi et al. introduced an ecological adaptive
cruise control (Eco-ACC) system for plug-in hybrid EVs that
optimizes fuel economy and safety by utilizing nonlinear
MPC to adjust vehicle speed based on the surrounding en-
vironment and adapts its driving behavior accordingly. The
developed model is tested and compared with PID and linear
MPC controllers in simulations. The findings demonstrate
that utilizing Nonlinear Model Predictive Control signifi-
cantly improves total energy cost, with an increase of up to
19 percent. while maintaining vehicle safety. The Eco-ACC’s
performance limitations under specific traffic conditions and
the critical influence of the prediction horizon parameter
highlight the necessity for future research in adaptive systems.
Developing advanced algorithms for autonomous prediction
horizon optimization based on real-time traffic data can en-
hance Eco-ACC adaptability and efficiency across diverse
driving scenarios.
In [160], Zhu et al. presented an MPC approach for EVs to
accurately track a desired speed profile while ensuring safety
and stability. The proposed approach uses a kinematic model
to predict the vehicle’s future behavior and adjust control in-
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TABLE 11. Comparison of Integrated ECU Algorithms related to EV control
Ref. Control Method Objective Strength Limitation
[148] Dynamic Programming Con-
trol Algorithm
Optimal Torque Distribution and
energy efficiency for four in-
wheel motor drive (4IWMD) EV
Reduced energy consumption
by 23.01% (IM240), 23.12%
(NEDC), and 23.89% (WLTC).
Exhaustive nature of the Dynamic
Programming (DP) algorithm
demands significant computational
and time resources
[149] Model-Based Reinforcement
Learning (RL)
Reduce energy consumption by
optimizing vehicle speed under a
range of driving scenarios
Near-optimal performance of
93.8% is attained
RL algorithm’s complexity and con-
vergence time need to be improved
[150] New Loss Minimization Algo-
rithm (LMA)
Efficiency optimization of per-
manent magnet synchronous mo-
tor (PMSM) for EVs
Improved efficiency by about 4-
7% compared with the conven-
tional LMA methods
Limited to the steady-state opera-
tion of PMSM
[151] Multi-objective Genetic Algo-
rithm
Improvements in efficiency for
the electric system, battery life,
and fuel economy
significant increase in battery ef-
ficiency resulting in enhanced en-
ergy management strategy appli-
cable to diverse driving scenarios
Complex offline computations for
various driving conditions with re-
sults storage in optimum look-up ta-
bles
[152] Fractional Order Darwinian
Particle Swarm Optimization
(FODPSO)
FODPSO based Field oriented
control (FOC) to optimize motor
performance and improve energy
efficiency
Proposed model outperforms tra-
ditional Field oriented control
(FOC) and Direct torque control
(DTC) methods in terms of effi-
ciency
Limited performance comparison
with other optimization techniques
[153] Deep reinforcement learning
(DRL) combined with transfer
learning
To find the most effective and op-
timal energy management strat-
egy (EMS) for hybrid electric ve-
hicles (HEVs)
Transfer learning algorithm en-
hances the efficiency of EMS se-
lection
Only one exploration method (i.e,
adding noise for action selection) of
DRL is considered.
[154] Hierarchical MPC (model pre-
dictive control) strategy
Minimization of battery degrada-
tion to extend its life
The developed model achieves en-
ergy savings of 3.38% and signif-
icantly reduces battery aging by
29.15%
The hierarchical MPC control strat-
egy is not suited for cabin thermal
management scenario in winter
[155] Model predictive control
(MPC)
Predictive braking control ap-
proach for EVs is presented
The torque tracking error is re-
duced by up to 60%, while there
is an improvement of up to 10%
in deceleration during emergency
braking
MPC can be made more robust
with online algorithms for estimat-
ing tire-road friction and optimal
wheel-slip
[156] Modular observer combined
with hierarchical feedback
control
Control of regenerative braking Stability of the proposed control
algorithm is analyzed using input-
to-state stability theory
Reliance on longitudinal tire forces
for velocity estimation.
[157] Predictive torque control (PTC) Energy saving for electric mo-
tor with decreased motor copper
losses
Outperforms DTC with reduced
torque ripple, lower Total har-
monic distortion (THD) in stator
current, less noise, and improved
speed tracking accuracy
Challenges in tuning weighting fac-
tors for Predictive Torque Control
(PTC)
[158] Speed-sensorless finite control
set-predictive torque control
(FSC-PTC)
Improvement in the control per-
formance of the IM drive by esti-
mating the load torque
Improved control performance in
various operating conditions by
combining adaptive fading ex-
tended Kalman filter with the pro-
posed control technique
Proposed method may have limita-
tions in high-speed applications due
to the finite control set.
[159] Ecological Adaptive Cruise
Control (ECO-ACC) with
Non-linear Model Predictive
Control (NMPC)
Fuel economy and safety by op-
timally adjusting vehicle speed
based on upcoming trip data
Improve in energy cost to about
19%
Careful tuning of prediction horizon
parameter for optimal performance
across diverse driving conditions.
[160] Model predictive control Implementation of speed control
strategy based on MPC
Safe and stable speed tracking
with minimum error while consid-
ering the constraints
Only the speed tracking aspect of
autonomous driving is considered
avoiding other aspects which may
affect the proposed system perfor-
mance.
[161] Model predictive control
(MPC)
Minimize the battery aging while
maintaining good braking perfor-
mance
Effective management of the bat-
tery current and braking force dis-
tribution
The proposed strategy is not tested
for different driving styles or road
conditions
[162] Parameterized model predic-
tive control
Steering performance improve-
ment of EVs
Real-time optimization of control
inputs are employed to enhance
the steering performance of EVs.
Sensitivity to prediction horizon re-
quires precise tuning for real-world
applications.
[163] Global search algorithm com-
bined with Model predictive
controller (MPC)
Improvement in energy
efficiency of in-wheel motors
through optimal torque
distribution
Reduces energy consumption by
21.66% and 11.18% in specific
maneuvers; 10.13% in slalom test
Using only the wheel slip ratio to
optimize torque.
[164] Modular Optimal Control
(High-Level Model Predictive
Control and Low-Level Torque
Regulation)
Integrated Longitudinal and Lat-
eral Vehicle Stability to prevent
real-time accidents
The proposed system enhances
vehicle stability in challenging
driving maneuvers compatible
with various actuation systems
Limited validation on vehicles with
active steering and differential brak-
ing systems.
VOLUME 11, 2023 19
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puts accordingly. Simulation results validate the effectiveness
of the proposed approach in achieving the desired speed pro-
file with minimal tracking error and smooth control actions.
The proposed approach only considers speed tracking and
does not address other aspects of autonomous driving, such
as path planning, obstacle avoidance, and decision-making.
The approach may be sensitive to the model’s parameters and
assumptions, affecting its performance in different scenarios.
In [161], Wu et al. introduced a hierarchical control strat-
egy for regenerative braking in hybrid electric vehicles con-
sidering battery aging. The strategy involves two levels of
control: a high-level controller that determines the braking
force distribution and a low-level controller that regulates the
battery current. The proposed approach is evaluated through
experimentation and simulation, and the results affirm that it
can effectively prolong the battery lifespan while maintaining
good braking performance. This study has a few areas for
improvement in that it focuses solely on the battery aging
aspect of the regenerative braking system and does not con-
sider other factors that could affect the battery lifespan, such
as temperature and cycling rate. Also, this study assumes
a specific driving scenario. It does not consider the effects
of different driving styles or road conditions, which could
impact the performance of the regenerative braking system
and the battery’s lifespan.
In [162], Murilo et al. proposed a parameterized model
predictive control approach for the electric power-assisted
steering system, enabling tuning control parameters to meet
different performance objectives and handle various operat-
ing conditions. The approach relies on the Electric Power
Assisted Steering (EPAS) system model, utilizing real-time
optimization of control inputs to enhance the steering perfor-
mance of EVs. The simulation results demonstrate that the
proposed control strategy can outperform traditional control
methods, indicating its potential to enhance the overall steer-
ing performance of EPAS systems. Future research might im-
prove the prediction horizon selection procedure to improve
the parameterized MPC’s robustness. Furthermore, doing a
field test on a vehicle will yield important information on how
well the suggested control technique works in actual EPAS
systems.
In [163], Changqing et al. suggested an integrated control
mechanism for EVs using active front steering and MPC
control. The strategy aims to enhance the vehicle’s stability,
energy efficiency, and driving comfort by coordinating torque
distribution between front and rear wheels and adjusting the
steering angle. Simulations show that the proposed strategy
surpasses traditional handling and energy consumption con-
trol. The results indicate that an integrated approach can
enhance the overall performance of EVs. One potential limi-
tation of this study is that it only considers improving driving
torque distribution based on the wheel slip ratio, potentially
ignoring other important aspects that significantly impact
the vehicle’s performance and energy economy. Subsequent
investigations may examine a more thorough optimization
strategy considering other variables besides the wheel slip
ratio. This might result in a more comprehensive and sophis-
ticated control approach for EVs, considering environmental
parameters, vehicle speed, and road surface conditions.
In [164], Nahidi et al. introduced a modular optimal control
method to highlight the challenge of integrated lateral and
longitudinal stability control for EVs. The proposed system
utilizes high and low-level controllers to regulate yaw mo-
ment and longitudinal force, enabling real-time optimization
of torque distribution among the wheels. The experimental
tests on an electric Chevrolet Equinox show improved vehicle
stability and dynamic response. While the system exhibits
adaptability to various vehicles and compatibility with var-
ious actuation systems, a more thorough study should in-
clude a wider range of EVs and actuation systems. To ensure
practical application, future research should concentrate on
expanding the control structure to a larger range of vehicles
and actuation systems.
Furthermore, EV data is acquired from the Kaggle website
to investigate the hidden patterns and trends of the different
models of Tesla EV [165]. This way, different analysis meth-
ods, such as correlation, boxplot, and comparison analysis,
are adopted to investigate the EV data. Our analysis revealed
interesting insights into the relationship between the selected
performance metrics and energy efficiency of electric cars
from Tesla, as shown in Figure 15. Specifically, we inves-
tigated the correlation between the following variables: Ac-
celeration Time (AccelSec), Top Speed (TopSpeed_KmH),
Range (Range_Km), Fast Charge (KmH) and Energy Effi-
ciency (Efficiency_WhKm).
FIGURE 15. Correlation of selected performance metrics and energy
efficiency of EVs
We employed the Pearson correlation coefficient to quan-
tify the degree of correlation between variables. The Pearson
correlation coefficient measures the linear relationship be-
tween two variables, ranging from -1 to +1. A value close to
+1 indicates a strong positive correlation, while a value close
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to -1 indicates a strong negative correlation. A value close to
0 suggests a weak or no correlation.
To visually represent the correlations, a correlation
heatmap is generated as shown in Figure 16. The heatmap il-
lustrates the strength and direction of the correlations between
the variables of interest. The color gradient in the heatmap
helps intuitively identify the correlations’ magnitude. The
resulting correlation heatmap indicates that top speed has the
strongest positive correlation with energy efficiency, followed
by range and fast charge with a slightly lower correlation.
Acceleration time shows a negative correlation with effi-
ciency, indicating that if acceleration time increases, then the
efficiency of the EV decreases. Furthermore, the correlation
analysis and heatmap provide valuable insights into the re-
lationship between key performance metrics and the energy
efficiency of electric cars from Tesla. These findings can as-
sist researchers, manufacturers, and policymakers understand
the factors influencing energy efficiency in electric vehicles.
The findings of the correlation analysis are summarized as
listed below:
Acceleration Time (AccelSec) vs. Energy Efficiency
(Efficiency_WhKm): A moderate negative correlation
is observed between the acceleration time and energy
efficiency of electric cars (-0.38). This suggests that
vehicles with high acceleration time have lower energy
efficiency.
Top Speed (TopSpeed_KmH) vs. Energy Efficiency
(Efficiency_WhKm): A positive correlation is observed
between the top speed of EVs and their energy efficiency
(0.36). This implies that vehicles with higher top speeds
exhibit higher energy efficiency.
Range (Range_Km) vs. Energy Efficiency (Effi-
ciency_WhKm): A moderate positive correlation is ob-
served between the range of electric cars and their energy
efficiency (0.31). This implies that vehicles with a longer
range tend to exhibit higher energy efficiency.
Fast Charge (Km_H) vs. Energy Efficiency (Effi-
ciency_WhKm): A positive correlation is observed be-
tween the fast charge and energy efficiency of EV (0.3).
This implies that vehicles with a fast charge exhibit
higher energy efficiency.
In addition to the correlation study results regarding rapid
charging, it is critical to understand the fundamental trade-
off of fast charging rates because they may result in a shorter
battery life. Xie et al. [166] provided a thorough analysis that
clarified important variables in this trade-off. Their results
highlight the need for a balanced approach, as they show that
high-rate charging can have mechanical effects on battery
components, such as physical stresses and strains, thermal
runaway (a phenomenon marked by an uncontrollably high
battery temperature), and even loss of lithium inventory,
which denotes a gradual decrease in the amount of lithium
that is available in the battery cells. In [166], optimum charg-
ing algorithms for Li-ion batteries are examined to avoid
battery degradation and achieve the shortest charging interval.
FIGURE 16. Correlation Heatmap of selected performance metrics and
energy efficiency of EVs
Therefore, as we explore the benefits of rapid charging for
energy efficiency, we must balance these advantages with
potential effects on battery life. This comprehensive approach
ensures that the development and utilization of fast charging
for electric vehicles (EVs) consider both performance en-
hancement and the long-term sustainability of the EV batter-
ies.
VIII. SHORTCOMINGS OF AVAILABLE SOLUTIONS AND
FUTURE DIRECTIONS
Considering the findings presented in our study on the de-
velopment strategies and algorithms of an integrated ECU
for mobility energy efficiency, it becomes apparent that sig-
nificant progress has been made in the overall EV control,
including VCU, EPS, ESC, and BCU. Even with these de-
velopments, it is clear that every method and algorithm still
needs more advancement to guarantee a sustainable future for
the broad use of EVs.
Shortcomings of Integrated ECU Development: De-
spite the integration of predictive analytics, machine
learning, thermal management, power electronics, and
energy recuperation techniques, real-time data process-
ing and accuracy in predictive modeling persist.
Shortcomings of VCU Development: While advance-
ments in sensor integration, powertrain component op-
timization, and intelligent energy management have en-
hanced Vehicle Control Units (VCUs), issues concern-
ing the interoperability of different components and
standardized communication protocols still pose chal-
lenges.
Shortcomings of EPS Development: Electric Power
Steering (EPS) systems have come a long way by in-
troducing high-efficiency motors and regenerative en-
ergy harvesting. Yet, achieving a balance between power
efficiency and steering precision, especially in diverse
driving conditions, remains a challenge.
VOLUME 11, 2023 21
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Shortcomings of ESC Development: While smooth
driving and intelligent sensor fusion have improved the
driving experience, thermal management and sensor ac-
curacy challenges during extreme driving scenarios need
to be addressed for enhanced safety and stability.
Shortcomings of BCU Development: Power manage-
ment, thermal regulation, and V2V communication in
Battery Control Units (BCUs) have limitations, partic-
ularly in managing power distribution optimally and
ensuring seamless communication between vehicles.
The solutions of the problems mentioned above are sum-
marized in Table 12 below:
TABLE 12. Problems and Solutions in EV Control Research
Problems Solutions
Limited predictive ana-
lytics accuracy
Implementation of advanced machine
learning algorithms for predictive
modeling, along with integrating real-
time feedback loops for data validation, is
required.
Insufficient real-time
data processing
Research on high-speed processors and
hardware accelerators for real-time data
processing and developing parallel com-
puting techniques needs to be researched.
Limited adaptability to
diverse driving condi-
tions
Research is required to implement AI-
based algorithms that analyze driving pat-
terns, weather conditions, and traffic data
to develop adaptive energy management
systems.
Insufficient cybersecu-
rity measures for VCU
communication
Strong encryption methods and authen-
tication procedures are needed to avoid
emerging risks and weaknesses and pro-
tect VCU communication networks.
Limited adaptive steer-
ing control technology
Future research must focus on creating
AI-based systems that use adaptive steer-
ing control, which modifies responsive-
ness and sensitivity to assess driving be-
havior and road conditions in real-time.
Limited accuracy
in sensor fusion
algorithms
More research is needed to improve sensor
fusion algorithms, enable real-time data
integration from many sensors, and in-
crease sensor data interpretation and accu-
racy through machine learning techniques.
Inadequate thermal con-
trol in BCU components
By creating passive and active thermal
management systems suited to the in-
dividual requirements of BCU compo-
nents, researchers may investigate ef-
fective thermal insulation materials and
cooling strategies to control temperatures
within the BCU.
Focusing on the challenges mentioned above and working
on possible solutions to these problems can open potential
directions for future research to improve energy efficiency
and ensure seamless integration of all control units. By di-
recting research efforts toward refining control strategies and
algorithms, researchers can contribute to EV systems’ overall
efficiency and safety, thus laying the groundwork for future
advancements in this vital aspect of integrated ECUs.
Moreover, it would be beneficial to expand the analysis
by contrasting the conventional control units in non-EVs to
explore potential areas for research in EV control units. This
comparative analysis may highlight important differences or
similarities in the development processes, offering sugges-
tions for future advances across disciplines. Furthermore, as
this survey focuses on increasing energy efficiency, future
studies may examine the relationship between electrical con-
trol units and sustainability. An in-depth understanding of
the wider implications of EV control technology would be
possible by considering environmental factors and investigat-
ing how the findings contribute to more environment-friendly
electric mobility solutions.
IX. CONCLUSION
In this comprehensive study, we have examined and detailed
the development strategies and algorithms of integrated Elec-
tronic Control Unit (ECU) and their crucial subsystems, in-
cluding Vehicle Control Unit (VCU), Electrical Power Steer-
ing (EPS), Electronic Stability Control (ESC), and Body Con-
trol Unit (BCU). By thoroughly examining different develop-
ment strategies and algorithms for each control unit, we have
highlighted their importance in attaining efficient control in
EVs, ultimately resulting in improved energy efficiency.
Our research has emphasized the paramount importance
of an integrated ECU in optimizing the performance and
efficiency of diverse vehicle systems. The integrated ECU
enables energy management, refines vehicle dynamics, and
significantly enhances overall energy efficiency by facilitat-
ing seamless communication and coordinated control actions
across subsystems. A vital aspect of our research includes
carefully examining sensors incorporated into every control
module. Precise control decisions are greatly aided by these
sensors, which record real-time data on acceleration, steering
angle, yaw rate, battery state of charge, and ambient factors.
By utilizing this data, the integrated ECU can optimize energy
use and increase the efficiency of the vehicle by making well-
informed modifications.
Additionally, our study has included a thorough review
of the state-of-the-art control methods presented in recent
research publications, providing a strong basis for our in-
vestigation and preparing for further research in this area.
The knowledge gathered from this in-depth analysis offers
researchers useful avenues to explore the field of EV control
systems further, enabling continued progress in the hunt for
more energy-efficient mobility solutions.
X. ACKNOWLEDGEMENT
SYED SHEHRYAR ALI NAQVI and FAIZA QAYYUM
contributed toward conceptualization, methodology, and
software. Furthermore, SYED SHEHRYAR ALI NAQVI,
HARUN JAMIL, and FAIZA QAYYUM performed a formal
analysis and prepared an original draft. Similarly, NAEEM
IQBAL, MURAD ALI KHAN, and Do-HYEUN KIM con-
tributed toward data curation, visualization, and supervision.
In addition, Salabat Khan reviewed and edited the original
draft and validated and investigated the overall manuscript.
The authors declared no conflict of interest regarding publish-
ing the role of Evolving Electric Mobility: In-Depth Analysis
of Integrated Electronic Control Unit Development in Electric
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Vehicles. (SYED SHEHRYAR ALI NAQVI, HARUN JAMIL
and FAIZA QAYYUM contributed equally to this work.)
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